ABSTRACT Artificial intelligence (AI) and internet technologies are transforming higher education by enabling adaptive and personalized learning environments. As students exhibit diverse cognitive abilities, cultural backgrounds, and learning preferences, conventional teaching approaches often fail to address individual needs effectively. This study proposes an Adaptive Hybrid Intelligent E‐Learning Platform (AHIELP) to support curriculum reform in Japanese higher education. The proposed framework integrates machine learning techniques, learning analytics, grey relational analysis, and intelligent decision‐making algorithms to optimize instructional delivery and enhance student learning outcomes. A controlled experimental design was conducted involving 150 undergraduate students divided into control and experimental groups through stratified randomization. The intervention was implemented over a 16‐week semester across STEM and Japanese language courses. Student performance was evaluated using five key metrics: learning efficiency, accuracy, performance, prediction capability, and precision. Statistical validation was performed using paired‐sample and independent‐sample t ‐tests to examine within‐group and between‐group differences. Results indicate that students using AHIELP demonstrated statistically significant improvements () across all performance indicators compared to those using conventional platforms. The system achieved 98% accuracy, 97% performance, 96% prediction accuracy, and 96% learning efficiency, highlighting its effectiveness in supporting adaptive learning pathways. The findings suggest that AI‐driven e‐learning systems can enhance instructional precision, reduce learning disparities, and contribute to sustainable and technology‐enhanced curriculum reform in Japanese higher education.
Parthasarathy et al. (Mon,) studied this question.